Improving the Performance of Artificial Intelligence and Robotics Systems through Comprehensive Sensor-Based Data Analysis and Predictive Model

This study looks at how robotics and artificial intelligence systems can work better through comprehensive sensor-based data analysis and predictive modeling. This research examines three important sensor parameters (Sensor 1, Sensor 2, and Sensor 3) and their combined impact on system performance indices in AI and robotics applications. Using a dataset of 30 observations, we used two It is possible to use machine learning techniques such as random forest regression and linear regression to predict and analyze the performance effects. The analysis reveals that Sensor 1 shows the strongest positive correlation with the performance index (r = 0.65), followed by Sensor 2 (r = 0.57), while Sensor 3 shows the weakest correlation (r = -0.043). The linear regression achieved an R² it was 0.92 in the training data, but dropped to 0.65 in the test data, indicating potential overfitting concerns. Random forest regression showed excellent training performance with an R² of 0.97, however, the testing performance decreased to 0.53, indicating challenges in model complexity. Descriptive analysis showed that sensor 1 (mean = 49.47, SD = 25.42) showed the highest variability, while sensor 3 (mean